Applied Machine Learning/Neural Networks: Offensive Security

Applied Machine Learning/Neural Networks: Offensive Security by Richard Knowell explores how machine learning, neural networks, and data-driven techniques intersect with penetration testing, cybersecurity research, and adversarial security.

As cybersecurity threats evolve, machine learning provides new ways to analyze malicious behavior, identify anomalies, automate security analysis, and investigate weaknesses in intelligent systems.

This practical guide introduces the relationship between artificial intelligence and offensive security, helping readers understand both the capabilities and security limitations of machine-learning technologies.

Inside, explore topics including:

• Machine learning and neural network fundamentals
• Supervised and unsupervised learning
• Classification, clustering, and anomaly detection
• Malware analysis using machine learning
• Threat detection and security analytics
• Adversarial machine learning concepts
• Model evasion and adversarial examples
• Data poisoning and model manipulation
• Security-focused automation and analysis
• Fuzzing and vulnerability research concepts
• Computer vision security considerations
• Python-based machine learning workflows
• Security applications of neural networks
• Practical tools, frameworks, and methodologies
• Offensive-security research and defensive strategies

Rather than examining machine learning as an isolated technology, this book explores how intelligent systems interact with real-world cybersecurity challenges and how attackers may exploit weaknesses in models, data, and automated decisions.

Designed for penetration testers, red team operators, security researchers, machine-learning enthusiasts, cybersecurity engineers, consultants, and students, this book provides a foundation for understanding the intersection of machine learning and offensive security.

Understand the Algorithms. Challenge the Models. Strengthen the Defenses.